code: def fdr_threshold(z_vals, alpha):
      """Return the Benjamini-Hochberg FDR threshold for the input z_vals.
  
      Parameters
      ----------
      z_vals : array
          A set of z-variates from which the FDR is computed.
  
      alpha : float
          The desired FDR control.
  
      Returns
      -------
      threshold : float
          FDR-controling threshold from the Benjamini-Hochberg procedure.
  
      """
      if alpha < 0 or alpha > 1:
          raise ValueError(
              f"alpha should be between 0 and 1. {alpha} was provided"
          )
      z_vals_ = -np.sort(-z_vals)
      p_vals = norm.sf(z_vals_)
      n_samples = len(p_vals)
      pos = p_vals < alpha * np.linspace(1 / n_samples, 1, n_samples)
      return z_vals_[pos][-1] - 1.0e-12 if pos.any() else np.inf
  
file_path: nilearn/nilearn/glm/thresholding.py
imports: import warnings
  import numpy
  from scipy.ndimage import label
  from scipy.stats import norm
  from nilearn.image import get_data, math_img, threshold_img
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 3 for calling a global function with example usage: fdr_threshold(z_vals, alpha) and returns: unknown